Q. In the context of supervised learning, what is a 'label'?
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A.
The input feature of the model
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B.
The output variable that the model is trying to predict
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C.
The algorithm used for training
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D.
The process of evaluating the model
Solution
In supervised learning, a label is the output variable that the model aims to predict based on input features.
Correct Answer:
B
— The output variable that the model is trying to predict
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Q. What does cross-validation help to prevent?
-
A.
Overfitting
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B.
Underfitting
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C.
Data leakage
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D.
Bias
Solution
Cross-validation helps to prevent overfitting by ensuring that the model performs well on unseen data.
Correct Answer:
A
— Overfitting
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Q. What is feature engineering in machine learning?
-
A.
The process of selecting the best model for a dataset
-
B.
The process of creating new features from existing data
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C.
The process of tuning hyperparameters of a model
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D.
The process of evaluating model performance
Solution
Feature engineering involves creating new features from existing data to improve model performance.
Correct Answer:
B
— The process of creating new features from existing data
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Q. What is the main advantage of using ensemble methods?
-
A.
They are simpler to implement than single models
-
B.
They can reduce variance and improve prediction accuracy
-
C.
They require less data for training
-
D.
They are always faster than individual models
Solution
Ensemble methods combine multiple models to reduce variance and improve overall prediction accuracy.
Correct Answer:
B
— They can reduce variance and improve prediction accuracy
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Q. What is the main goal of feature scaling?
-
A.
To reduce the number of features
-
B.
To ensure all features contribute equally to the distance calculations
-
C.
To improve the interpretability of the model
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D.
To increase the complexity of the model
Solution
Feature scaling ensures that all features are on a similar scale, which is important for distance-based algorithms.
Correct Answer:
B
— To ensure all features contribute equally to the distance calculations
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Q. What is the main goal of model selection?
-
A.
To find the most complex model
-
B.
To choose the model with the highest accuracy on the training set
-
C.
To identify the model that generalizes best to unseen data
-
D.
To minimize the number of features used
Solution
The main goal of model selection is to identify the model that generalizes best to unseen data.
Correct Answer:
C
— To identify the model that generalizes best to unseen data
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Q. What is the purpose of hyperparameter tuning?
-
A.
To select the best features
-
B.
To improve model performance by optimizing parameters
-
C.
To evaluate model accuracy
-
D.
To visualize data distributions
Solution
Hyperparameter tuning aims to improve model performance by optimizing the parameters that govern the learning process.
Correct Answer:
B
— To improve model performance by optimizing parameters
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Q. Which feature scaling technique centers the data around zero?
-
A.
Min-Max Scaling
-
B.
Standardization
-
C.
Normalization
-
D.
Log Transformation
Solution
Standardization centers the data around zero by subtracting the mean and dividing by the standard deviation.
Correct Answer:
B
— Standardization
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Q. Which of the following is a common method for feature extraction?
-
A.
K-means Clustering
-
B.
Support Vector Machines
-
C.
Principal Component Analysis
-
D.
Decision Trees
Solution
Principal Component Analysis (PCA) is a technique used to reduce dimensionality by extracting important features.
Correct Answer:
C
— Principal Component Analysis
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Q. Which of the following is a method for handling missing data?
-
A.
Normalization
-
B.
Imputation
-
C.
Regularization
-
D.
Feature Scaling
Solution
Imputation is a technique used to fill in missing values in a dataset.
Correct Answer:
B
— Imputation
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Q. Which of the following is NOT a common technique for feature selection?
-
A.
Recursive Feature Elimination
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B.
Principal Component Analysis
-
C.
Random Forest Importance
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D.
Gradient Descent
Solution
Gradient Descent is an optimization algorithm, not a feature selection technique.
Correct Answer:
D
— Gradient Descent
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Q. Which of the following is NOT a feature engineering technique?
-
A.
Binning
-
B.
Feature Extraction
-
C.
Data Augmentation
-
D.
Gradient Descent
Solution
Gradient Descent is an optimization algorithm, not a feature engineering technique.
Correct Answer:
D
— Gradient Descent
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Q. Which of the following techniques is used for dimensionality reduction?
-
A.
K-Means Clustering
-
B.
Support Vector Machines
-
C.
Principal Component Analysis
-
D.
Decision Trees
Solution
Principal Component Analysis (PCA) is a widely used technique for reducing the dimensionality of data.
Correct Answer:
C
— Principal Component Analysis
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